Abnormal Group-Based Joint Medical Fraud Detection

Abstract

Joint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters. However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns. To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method. This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by non-fraudulent abnormal behavior can be reduced. The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods.